""" Real AI Pipeline using YOLO and CLIP Production-ready image analysis with real models """ from typing import Dict, List, Optional, Tuple from dataclasses import dataclass import numpy as np from PIL import Image import io # Try to import real AI libraries try: import torch import torchvision from transformers import CLIPProcessor, CLIPModel HAS_REAL_AI = True except ImportError: HAS_REAL_AI = False print("Warning: Real AI libraries not installed. Using simulation.") @dataclass class AIAnalysisResult: """Result from real AI analysis""" detected_objects: List[Dict] defect_codes: List[Dict] scene_type: str confidence: float embeddings: Optional[np.ndarray] = None def to_dict(self) -> Dict: return { "detected_objects": self.detected_objects, "defect_codes": self.defect_codes, "scene_type": self.scene_type, "confidence": self.confidence, "has_embeddings": self.embeddings is not None } class RealAIClassifier: """ Production AI classifier using YOLO and CLIP Requirements: - torch - torchvision - transformers - pillow - numpy Models: - YOLOv8 for object detection - CLIP for scene classification - Custom classifier for defects """ def __init__(self, use_real_ai: bool = True): self.use_real_ai = use_real_ai and HAS_REAL_AI self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if HAS_REAL_AI else None # Load models self.yolo_model = None self.clip_model = None self.clip_processor = None self.defect_classifier = None if self.use_real_ai: self._load_models() else: print("Using simulated AI (install torch, torchvision, transformers for real AI)") def _load_models(self): """Load AI models""" print("Loading AI models...") # Load YOLOv8 try: from ultralytics import YOLO self.yolo_model = YOLO("yolov8n.pt") # Nano model for speed print("✓ YOLOv8 loaded") except Exception as e: print(f"✗ YOLOv8 failed: {e}") self.yolo_model = None # Load CLIP try: self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") self.clip_model.to(self.device) print("✓ CLIP loaded") except Exception as e: print(f"✗ CLIP failed: {e}") self.clip_model = None self.clip_processor = None # Load defect classifier (custom model) try: # In production, load trained model # self.defect_classifier = torch.load("defect_classifier.pth") print("✓ Defect classifier placeholder loaded") except Exception as e: print(f"✗ Defect classifier failed: {e}") self.defect_classifier = None def analyze_image(self, image_path: str, location: Optional[Dict] = None) -> AIAnalysisResult: """Analyze image with real AI""" if not self.use_real_ai: return self._simulate_analysis(image_path, location) # Load image image = Image.open(image_path).convert("RGB") # Object detection with YOLO detected_objects = self._detect_objects(image) # Scene classification with CLIP scene_type, scene_confidence = self._classify_scene(image) # Defect detection defect_codes = self._detect_defects(image) # Generate embeddings embeddings = self._generate_embeddings(image) return AIAnalysisResult( detected_objects=detected_objects, defect_codes=defect_codes, scene_type=scene_type, confidence=scene_confidence, embeddings=embeddings ) def _detect_objects(self, image: Image.Image) -> List[Dict]: """Detect objects with YOLO""" if self.yolo_model is None: return [] results = self.yolo_model(image) objects = [] for result in results: boxes = result.boxes for box in boxes: objects.append({ "label": result.names[int(box.cls)], "confidence": float(box.conf), "bbox": box.xyxy.tolist()[0] }) return objects def _classify_scene(self, image: Image.Image) -> Tuple[str, float]: """Classify scene with CLIP""" if self.clip_model is None or self.clip_processor is None: return "unknown", 0.0 # Define scene labels scene_labels = [ "street view", "building facade", "bridge", "road", "sidewalk", "park", "industrial area", "residential area", "commercial area", "construction site" ] # Process image inputs = self.clip_processor( text=scene_labels, images=image, return_tensors="pt", padding=True ).to(self.device) # Get predictions with torch.no_grad(): outputs = self.clip_model(**inputs) logits_per_image = outputs.logits_per_image probs = logits_per_image.softmax(dim=1) # Get top prediction top_prob, top_idx = probs.max(dim=1) return scene_labels[top_idx.item()], float(top_prob.item()) def _detect_defects(self, image: Image.Image) -> List[Dict]: """Detect defects with custom classifier""" if self.defect_classifier is None: return [] # In production, use trained defect classifier # For now, return empty list return [] def _generate_embeddings(self, image: Image.Image) -> Optional[np.ndarray]: """Generate image embeddings with CLIP""" if self.clip_model is None or self.clip_processor is None: return None inputs = self.clip_processor( images=image, return_tensors="pt" ).to(self.device) with torch.no_grad(): image_features = self.clip_model.get_image_features(**inputs) return image_features.cpu().numpy() def _simulate_analysis(self, image_path: str, location: Optional[Dict] = None) -> AIAnalysisResult: """Simulated analysis when real AI is not available""" from ai_pipeline.image_classifier import ImageClassifier simulator = ImageClassifier() result = simulator.analyze_image(image_path, location) return AIAnalysisResult( detected_objects=result.detected_objects, defect_codes=result.defect_codes, scene_type=result.scene_type, confidence=result.confidence, embeddings=None ) def batch_process(self, image_paths: List[str], locations: Optional[List[Dict]] = None) -> List[AIAnalysisResult]: """Process multiple images""" results = [] for i, path in enumerate(image_paths): loc = locations[i] if locations and i < len(locations) else None result = self.analyze_image(path, loc) results.append(result) return results # Example usage def example_real_ai(): """Example: Real AI analysis""" print("=== Real AI Pipeline ===\n") classifier = RealAIClassifier(use_real_ai=False) # Set to True when models are installed print(f"Real AI available: {classifier.use_real_ai}") print(f"Device: {classifier.device}") # Analyze image result = classifier.analyze_image("test_image.jpg") print(f"\nDetected objects: {len(result.detected_objects)}") print(f"Scene type: {result.scene_type}") print(f"Confidence: {result.confidence}") print(f"Has embeddings: {result.embeddings is not None}") return result if __name__ == '__main__': example_real_ai()